The role of PI3-Kinase signalling in immunomodulation by a protective innate defence regulator peptide (135.6)
Bibliographic record
Abstract
Abstract The rise of antibiotic resistance has lead to the search for new therapeutics against bacterial infections. Innate defence regulators (IDRs), synthetic versions of natural host defence peptides, are being developed as anti-infective agents due to their immunomodulatory properties. HDPs have been shown to suppress infections by boosting leukocyte recruitment, while limiting harmful inflammation. To optimize the IDR regulatory function, we developed peptide 1002, a bactenecin derivative, by screening a peptide library for enhanced leukocyte chemoattractants induction in human blood mononuclear cells (hBMC). 1002 enhanced protection in a Staphylococcus aureus murine infection model, correlating with increased chemokine production and leukocyte recruitment. 1002 also suppressed inflammatory cytokine production by lipopolysaccharide-stimulated hBMC. IDRs are being deciphered to assist in their development as therapeutics. We found that 1002 regulation is dependent on the PI3-Kinase pathway, a signal cascade that regulates immune and inflammatory responses. PI3-K inhibition abrogated 1002-mediated chemokine induction and reduced its anti-inflammatory properties. 1002 influence on PI3-K signalling was confirmed by examining responses of pathway elements and by systems biology microarray analysis of the immune network by InnateDB. Understanding the mechanisms of IDR immune regulation via PI3-K modulation will aid in their development as novel anti-infective agents. This work was supported by Genome BC and Genome Prairie for the Pathogenomics of Innate Immunity Research Program, and by FNIH and CIHR through the Grand Challenges in Global Health Initiative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".